Sensor-based monitoring can be used in a variety of industrial settings. Power generating systems, manufacturing processes, and a host of other industrial operations involving the coordinated functioning of large-scale, multi-component systems can all be efficiently controlled through sensor-based monitoring. Indeed, sensor-based monitoring can be advantageously employed in virtually any environment in which various system-specific parameters need to be monitored over time under varying conditions.
The control of a system or process typically entails monitoring various physical indicators under different operating conditions, and can be facilitated by sensor-based monitoring. Monitored indicators can include temperature, pressure, flows of both inputs and outputs, and various other operating conditions. The physical indicators are typically monitored using one or more transducers or other type of sensors.
An example of a system with which sensor-based monitoring can be advantageously used is an electrical power generation system. The generation of electrical power typically involves a large-scale power generator such as a gas or steam turbine that converts mechanical energy into electrical energy through the process of electromagnetic induction to thereby provide an output of alternating electrical current. A power generator typically acts as reversed electric motor, in which a rotor carrying one or more coils is rotated within a magnetic field generated by an electromagnet. Important operating variables that should be closely monitored during the operation of a power generator include pressure and temperature in various regions of the power generator, as well as the vibration of critical components. Accordingly, sensor-based monitoring is a particularly advantageous technique for monitoring the operation of a power generator.
Regardless of the setting in which it is used, a key task of sensor-based monitoring can be to evaluate data provided by a multitude of sensors. This can be done so as to detect and localize faults so that the faults can be corrected in a timely manner. Within a power generating plant, in particular, the timely detection of faults can prevent equipment damage, reduce maintenance costs, and avoid costly, unplanned shutdowns.
Monitoring typically involves receiving sensor-supplied data, which can be mathematically represented in the form of sensor vectors or scalars, defined herein simply as sensor values. These sensor values provide data input into a model and are compared with estimated output values obtained by applying the model to the data input. Large deviations between the actual sensor values and the estimated sensor values generated by the model can indicate that a fault has occurred or is about to occur. Accordingly, accurate monitoring can depend critically on the accuracy of the model employed.
There are principally two approaches to constructing such a model. The first approach is referred to as principle or physical modeling, and involves constructing a largely deterministic model representing the physical phenomena that underlie the operation of a particular system or process. It can be the case, however, that the physical dimensions of the system are too numerous or too complex to lend themselves to an accurate representation using the physical model. Accordingly, it is sometimes necessary to resort to the second approach, that of statistical modeling. Sensor-based monitoring of a power generation system, largely because it can require the use of literally hundreds of sensors, can necessitate the construction of such a statistical model. Constructing a statistical model involves “training” a probabilistic model using historical data samples of the system. The purpose of training the model is to glean from the historical data the distribution of the sensor vectors when the system is operating normally.
The probabilistic nature of sensor-based monitoring using a statistical model adds to the burden that inheres in any type of sensor-based monitoring, that of differentiating between a true system fault and an erroneous fault indication that is the result of a defective sensor. The need to differentiate a true fault indication from an erroneous one can be particularly acute in a power generation system. A shutdown in response to an erroneously indicated fault is not only inconvenient but can be very costly. Conversely, as already noted, failure to identify a system fault before or quickly after it occurs can lead to equipment damage and even longer shut-downs when such equipment must be repaired or replaced as a result of the failure. Accordingly, sensor-based monitoring should include an ability to identify and isolate a faulty sensor in a timely manner.
Conventional techniques for detecting a faulty sensor typically rely on, or result, in a reduction of the dimensionality of input data sets. Any reduction in dimensionality, however, has a concomitant adverse impact on the accuracy of the model used. Moreover, conventional techniques used in conjunction with probabilistic sensor-based monitoring require the underlying statistical model to be linear in nature. This linearity requirement can be problematic if the underlying system can not be adequately represented by a linear model.
Accordingly, there is a need for better systems and methods of identifying and isolating a faulty sensor. There is especially a need for a system and method that identifies and isolates a faulty sensor without having to reduce the dimensionality of the training data. There is a further need for a system and method of identifying and isolating a faulty sensor without requiring that an underlying statistical model be linear.